GrowSplat: Constructing Temporal Digital Twins of Plants with Gaussian Splats

Accurate temporal reconstructions of plant growth can be valuable for plant phenotyping and breeding, yet remain challenging due to complex geometries, occlusions, and non-rigid deformations of plants. We present GrowSplat, a novel framework for building temporal digital twins of plants by combining 3D Gaussian Splatting with a robust spatial alignment pipeline. GrowSplat begins by constructing a temporal sequence of Gaussian Splats from multi-view camera data, then performs a two-stage spatial registration approach: coarse alignment through feature-based matching and Fast Global Registration, followed by fine alignment with Iterative Closest Point. This pipeline yields a consistent 4D model of plant development in discrete time steps. We evaluate the approach on data from the Netherlands Plant Eco-phenotyping Center, demonstrating detailed temporal reconstructions of Sequoia and Quinoa species. Videos and Images can be seen at https://berkeleyautomation.github.io/GrowSplat/